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Generating unlimited skin lesion images with generative adversarial networks

Grant number: 19/19619-7
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): June 01, 2020
Effective date (End): February 29, 2024
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Sandra Eliza Fontes de Avila
Grantee:Alceu Emanuel Bissoto
Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:13/08293-7 - CCES - Center for Computational Engineering and Sciences, AP.CEPID
Associated scholarship(s):22/09606-8 - Understanding the role of shortcuts and distribution shifts in deep learning generalization, BE.EP.DR

Abstract

Melanoma is the deadliest form of Skin Cancer. Automated skin lesion analysis can play an important role for early detection, which is crucial for patient's treatment. Deep neural networks are the current state-of-the-art for skin lesion analysis, but the lack of annotated data limits classification performance. We started in 2017 to generate annotated data for our classification models with Generative Adversarial Networks (GANs). A GAN can learn the data distribution, allowing us to sample from it, enabling a complementary method for data augmentation. In our previous work, we were able to generate high-resolution, clinically-meaningful skin lesion images that when used to augment a classification network's training data boosted performance. However, our method had limitations. The amount of synthetics we can generate is limited by a special annotation that is available only for a small subset of our training data. This limitation is troublesome because ideally we want to generate infinite amounts of data to aid skin lesion classification. In this Ph.D. proposal, we explore methods for infinite generation: we propose to incorporate relevant clinical information into the GAN generation process, maintaining quality without killing variability and quantity. We believe our experience in generative models and skin lesion analysis qualify this work, enabling us to improve the current state-of-the-art for skin lesion generation and classification. We highlight that our group is at the forefront of such research worldwide being responsible for groundbreaking results associated with skin lesion analysis. (AU)

Matéria(s) publicada(s) na Revista Pesquisa FAPESP sobre a bolsa::
La inteligencia artificial llega a la salud en Brasil 
Artificial intelligence in healthcare 
Diagnósticos digitales 
Digital diagnosis 
News published in Agência FAPESP Newsletter about the scholarship:
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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MIRIKHARAJI, ZAHRA; ABHISHEK, KUMAR; BISSOTO, ALCEU; BARATA, CATARINA; AVILA, SANDRA; VALLE, EDUARDO; CELEBI, M. EMRE; HAMARNEH, GHASSAN. A survey on deep learning for skin lesion segmentation. MEDICAL IMAGE ANALYSIS, v. 88, p. 40-pg., . (19/19619-7, 13/08293-7)
BISSOTO, ALCEU; VALLE, EDUARDO; AVILA, SANDRA; IEEE COMP SOC. GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review. 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGITION WORKSHOPS (CVPRW 2021), v. N/A, p. 10-pg., . (19/19619-7, 13/08293-7)

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